presatastion all draft

1.

ARCHITECTURAL AND
ALGORITHMIC
CHALLENGES IN
AUTONOMOUS
Volodymyr Kviatkivskyi
Learner No.20260916

2.

SENSOR FUSION AND COMPUTATIONAL CONSTRAINTS
• High Volume Data Ingestion: Heterogeneous sensor
network (LiDAR, RADAR, CMOS cameras) generate
approximately 4 Terabytes of raw date daily.
• Algorithmic Sensor Fusion: Software architecture
architectures must synchronize and fuse multi
modal data streams within microsecond constraints
to maintain a coherent spatial environment map.
• Hardware Overhead: Real time processing imposes
substantial thermal and power loads on vehicle
GPUs, threatening system throttling during peak
operational scenarios.
This Photo by Unknown

3.

DEEP LEARNING LIMITATIONS AND EDGE CASES
• Perception Frameworks: Object recognition and classification
subroutines rely heavily on Deep Convolutional Neural
Networks (CNNs).
• The Problem f “Edge Cases”:Stochastic machine learning models
struggle to accurately classify non standard obstacles or
unpredictable human behaioral patterns not represented in
baseline training datesets.
• Environmental Interference: severe weather anomalies (heavy
precipitation, dense fog, solar glare) introduce catastrophic
sensor noise.

4.

DETERMINISTIC ARCHITECTURE &REAL TIME OPERATING SYSTEMS
• Real Time Processing Latency: Standard operating systems exhibit non deterministic execution
times .Autonomous systems strictly require a Real Time Operating System (RTOS) to guarantee execution
safety.
• Predictive Kinematics: Trajectory planning subroutines must dynamically calculate vehicle kinematics and
predict pedestrian vector simultaneously.
• Latency Trade off: Optimizing algorithmic accuracy inherently increases computational overhead, generating a
critical design trade off between perception depth and safety critical response latency.

5.

CYBERSECURITY VULNERABILITIES AND VECTOR ATTACKS
• Codebase Complexity: Modern autonomous vehicle
software comprises over 100 million lines of source
code, expanding the potential attack surface for
logic exploits and software bugs.
• Adversarial Machine Learning: Neural networks are
highly vulnerable to adversarial attacks, where
subtle physical manipulations of road signage (e.g.
malicious stickers) cause critical classification
errors.
• Over The Air (OTA) Security: ensuring encrypted
firmware updates without compromising fail safe
architectures during vehicle runtime.

6.

ENGINEERING SYNTHESIS AND REDUNDANCY
• Fail Safe Design: Mitigating critical software failures
requires a highly redundant hardware
architecture featuring parallel, independent
processing units.
• The “Black Box” Problem: Validating deep learning
subroutines remains complex due to the opaque
nature of neural network weight distributions,
complicating standard code verification.
• Conlusion: Achieving reliable Level 5 autonomy
necessitates a standardized integration of robust
software validation frameworks, fail operational
hardware, and intelligent V2X infrastructure.